Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. We have developed an approach, called stochastic profiling, that applies probability theory to transcriptome-wide measurements of small pools of cells to identify single-cell regulatory heterogeneities (Nat Methods 7:311-7 [2010]). In the first half of the talk, I will discuss a two-state regulatory circuit that was identified by stochastic profiling (Nat Cell Biol 16:345-56 [2014]). The circuit involves TGF-family signaling and the junD transcription factor, which are asynchronously activated in 3D breast epithelial cultures to coordinate normal morphogenesis. The circuit also appears to be re-initiated during the early stages of basal-like breast cancer, contributing to the mosaicked expression patterns observed clinically by histology. In the second half of the talk, I will talk about work in progress that applies stochastic profiling as a tool for uncovering the mechanistic basis of phenotypes that are incompletely penetrant. Regulatory-state frequencies are matched to downstream phenotype frequencies to converge upon a tractable set of candidate states worth of follow-up experimentation. Using the ErbB2 oncoprotein as a model trigger for an incompletely penetrant phenotype, we identify a handful of surprising candidates that significantly affect penetrance when perturbed. Stochastic profiling remains the only method compatible with cells microdissected in situ and thereby opens exciting opportunities in the areas of tissue morphogenesis and cancer.